Tulsi P. Kharel, Heather L. Tyler, Partson Mubvumba, Yanbo Huang, Ammar B. Bhandari, Reginald S. Fletcher, Saseendran Anapalli, Deepak R. Joshi, Alemu Mengistu, Girma Birru, Kabindra Adhikari, Madhav Dhakal, Mahesh L. Maskey, Krishna N. Reddy, David E. Clay
{"title":"基于多光谱图像的机器学习估算混种覆盖作物的养分产量","authors":"Tulsi P. Kharel, Heather L. Tyler, Partson Mubvumba, Yanbo Huang, Ammar B. Bhandari, Reginald S. Fletcher, Saseendran Anapalli, Deepak R. Joshi, Alemu Mengistu, Girma Birru, Kabindra Adhikari, Madhav Dhakal, Mahesh L. Maskey, Krishna N. Reddy, David E. Clay","doi":"10.1002/ael2.70009","DOIUrl":null,"url":null,"abstract":"<p>This study aimed to estimate mixed-species cover crop (CC) biomass and nutrient contents using remote sensing, as ground-based measurements are time-consuming and costly. Eleven CC treatments with varying grass-legume proportions (GLP) were sampled, and nutrient contents were determined along with multispectral imagery captured during the first and fourth weeks of March and the fourth week of April 2023. Biomass N (<i>R</i><sup>2</sup> = 0.46–0.60) and K% (<i>R</i><sup>2</sup> = 0.41—0.71) decreased with increasing GLP. The chlorophyll absorption ratio index and the normalized difference vegetation index closely followed the biomass nutrients N, P, and K combined yield (Bio_NPK) trend. Machine learning algorithms random forest (RF) and partial least square (PLS) regression were better for biomass (<i>R</i><sup>2 </sup>= 0.74 with RF) and N% (<i>R</i><sup>2 </sup>= 0.72 with PLS) prediction compared to the Bio_NPK prediction. These results are crucial for scientists to devise appropriate analysis approaches for estimating the benefits of mixed-species CC.</p>","PeriodicalId":48502,"journal":{"name":"Agricultural & Environmental Letters","volume":"10 1","pages":""},"PeriodicalIF":2.3000,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ael2.70009","citationCount":"0","resultStr":"{\"title\":\"Machine learning on multi-spectral imagery to estimate nutrient yield of mixed-species cover crops\",\"authors\":\"Tulsi P. Kharel, Heather L. Tyler, Partson Mubvumba, Yanbo Huang, Ammar B. Bhandari, Reginald S. Fletcher, Saseendran Anapalli, Deepak R. Joshi, Alemu Mengistu, Girma Birru, Kabindra Adhikari, Madhav Dhakal, Mahesh L. Maskey, Krishna N. Reddy, David E. Clay\",\"doi\":\"10.1002/ael2.70009\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>This study aimed to estimate mixed-species cover crop (CC) biomass and nutrient contents using remote sensing, as ground-based measurements are time-consuming and costly. Eleven CC treatments with varying grass-legume proportions (GLP) were sampled, and nutrient contents were determined along with multispectral imagery captured during the first and fourth weeks of March and the fourth week of April 2023. Biomass N (<i>R</i><sup>2</sup> = 0.46–0.60) and K% (<i>R</i><sup>2</sup> = 0.41—0.71) decreased with increasing GLP. The chlorophyll absorption ratio index and the normalized difference vegetation index closely followed the biomass nutrients N, P, and K combined yield (Bio_NPK) trend. Machine learning algorithms random forest (RF) and partial least square (PLS) regression were better for biomass (<i>R</i><sup>2 </sup>= 0.74 with RF) and N% (<i>R</i><sup>2 </sup>= 0.72 with PLS) prediction compared to the Bio_NPK prediction. These results are crucial for scientists to devise appropriate analysis approaches for estimating the benefits of mixed-species CC.</p>\",\"PeriodicalId\":48502,\"journal\":{\"name\":\"Agricultural & Environmental Letters\",\"volume\":\"10 1\",\"pages\":\"\"},\"PeriodicalIF\":2.3000,\"publicationDate\":\"2025-01-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ael2.70009\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Agricultural & Environmental Letters\",\"FirstCategoryId\":\"97\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/ael2.70009\",\"RegionNum\":4,\"RegionCategory\":\"农林科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"AGRICULTURE, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Agricultural & Environmental Letters","FirstCategoryId":"97","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/ael2.70009","RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AGRICULTURE, MULTIDISCIPLINARY","Score":null,"Total":0}
Machine learning on multi-spectral imagery to estimate nutrient yield of mixed-species cover crops
This study aimed to estimate mixed-species cover crop (CC) biomass and nutrient contents using remote sensing, as ground-based measurements are time-consuming and costly. Eleven CC treatments with varying grass-legume proportions (GLP) were sampled, and nutrient contents were determined along with multispectral imagery captured during the first and fourth weeks of March and the fourth week of April 2023. Biomass N (R2 = 0.46–0.60) and K% (R2 = 0.41—0.71) decreased with increasing GLP. The chlorophyll absorption ratio index and the normalized difference vegetation index closely followed the biomass nutrients N, P, and K combined yield (Bio_NPK) trend. Machine learning algorithms random forest (RF) and partial least square (PLS) regression were better for biomass (R2 = 0.74 with RF) and N% (R2 = 0.72 with PLS) prediction compared to the Bio_NPK prediction. These results are crucial for scientists to devise appropriate analysis approaches for estimating the benefits of mixed-species CC.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
